• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.63-82
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• 1997

In experimental situations where n two or three level fac-tors are involoved and n observations are taken then the D-optimal first order saturated design is an $n{\times}n$ matrix with elements $\pm$1 or 0, $\pm$1, with the maximum determinant. Cononical forms are useful for the specification of the non-isomorphic D-optimal designs. In this paper we study canonical forms such as the Smith normal form the first sec-ond and the jordan canonical form of D-optimal designs. Numerical algorithms for the computation of these forms are described and some numerical examples are also given.

• Journal of Applied Reliability
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• v.9 no.1
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• pp.13-28
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• 2009

In this paper we propose a method for designing optimum degradation test based on nonlinear random-coefficients models. We use the approximated expression of the Fisher information matrix for nonlinear random-coefficients models. We apply the simplex algorithm to the inverse of the determinant of Fisher information matrix to satisfy the D-optimal criterion. By comparison of the results from PDP degradation data, we suggest a general guideline to obtain optimum experimental design for determining inspection intervals and number of samples in degradation testing.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.104-105
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• 2013

When actual IPMSM is driving, it is difficult to figure out the correct current during the current control period due to the operation speed limit of digital signal processing. Therefore, in order to control IPMSM for electric vehicle efficiently, we should design 2D Look-up Table to find out optimal current reference corresponding to speed and torque of IPMSM. This paper explains the design method of 2D Look-up Table for optimal current control of constant torque area and constant output area of IPMSM for electric vehicle. Finally, experimental results are presented to verify the reliability of 2D Look-up Table.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.1
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• pp.74-80
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• 2017

This study aimed to evaluate the validity of an exhausted injector design for filtration system and the performance characteristics thereof. The evaluation was intended through computational fluid dynamics(CFD) analysis based on computer simulation rather than through prototype fabrication and testing. Furthermore, the design of experiment was used to create an experimental design table by which the reaction characteristics of response factors were analyzed for design parameters. All experiments were substituted with computer simulations. Lastly, an optimal design model for the injection tubes was determined based on response surface method algorithms.

• Environmental Engineering Research
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• v.15 no.3
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• pp.129-134
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• 2010

The optimization of experimental parameters, such as catalyst type, catalyst concentration, molar ratio of alcohol to oil and reaction temperature, on the transesterification for the production of rapeseed methyl ester has been studied. The Taguchi approach (Taguchi method) was adopted as the experimental design methodology, which was adequate for understanding the effects of the control parameters and to optimize the experimental conditions from a limited number of experiments. The optimal experimental conditions obtained from this study were potassium hydroxide as the catalyst, at a concentration of 1.5 wt %, and a reaction temperature of $60^{\circ}C$. According to Taguchi method, the catalyst concentration played the most important role in the yield of rapeseed methyl ester. Finally, the yield of rapeseed methyl ester was improved to 96.7% with the by optimal conditions of the control parameters which were obtained by Taguchi method.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.5
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• pp.234-241
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• 2002

An optical pickup actuator is an objective-lens-moving mechanism that provides a means to follow the disk displacement accurately(1). In this paper, a slim type optical pickup actuator for Notebook PCs is analyzed and designed to improve the driving sensitivity A three dimensional equivalent magnetic circuit network method (3D-EMCN method) is proposed for an analysis method which provides better characteristics in both precision and computation time of analysis comparing with a commercial three-dimensional finite element (3D-FEM) codes. To verify the validity of proposed method, we made a comparison between the analysis results and the experimental ones. We also compared this analysis results with 3D-FEM results. Among the several optimal algorithm, we adopt a niching genetic algorithm, which renders a set of the multiple optimal solutions. RCS (Restricted Competition Selection) niching genetic algorithm is used for optimal design of the actuator's performance. Recently, the pickup actuator needs additional driving structure for radial and tangential tilting motion to obtain better pick-up performance. So we applied the proposed method to the model containing tilting coils.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.186-191
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• 2015

Presently, rapidly changing and unstable global economic environments demand engineers. Products should be designed to increase profits by lowering costs and provide distinguished performance compared with competitors. This study aims to optimize the design of the power-transmission drive shaft. The mass is reduced as an objective function, and the stress is constrained under a constant value. To reduce the number of experiments, CCD (central composite design) and D-Optimal are used for the experimental design. RSM (response surface methodology) is employed to construct a regression model for the objective functions and constraint function. In this problem, there is only one objective function for the mass. The other objective function gives 1; thus, NSGA-II is used.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.303-309
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• 1993

In this paper we have defined two approaches to be error-robust when the precise form of error-structure is unknown. An experiment is optimal by the first criterion if it maximizes the minimum effciency over all candidates of error structure and is optimal by the second if it maximizes the minimum average of the efficiency over all candidates of error structure. In order to appreciate the basic implications of each design criterion, these approaches are applied to two different experimental situations, D- and heteroscedastic G-optimalities.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1491-1498
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• 2014

The major criticism of optimal experimental design is that it depends heavily on the model and its accompanying assumption that often leads the number of support points equal to the number of parameters in the model. Often in the past, a polynomial model of higher degree is assumed to handle the experimental design for the polynomial regression of lower degree. In this paper we searched the possible set of designs which are robust to the departure of the assumed model. The designs are categorized with respect to D-efficiency. The approach by O'Brien (1995) was discussed in univariate polynomial regression model setting.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.